Sepsis is an ongoing concern in critical care. It is hard to quickly detect, and rapid deterioration of a patient into septic shock causes death in around 30% - 50% of patients, while survivors may live with organ damage and shorter lifespans. Traditional methods of detection require long laboratory tests and clinician vigilance, which put a strain on hospital resources.
New advances in machine learning offer an alternative – using algorithmic analysis in real-time to watch for a deteriorating patient state. The use of readily available data – heart rate, respiratory rate – combined with electronic medical records and fast laboratory tests presents an opportunity for early detection of sepsis, which can potentially make great strides in minimizing damage to patients.
A variety of algorithmic methods have been proposed by researchers, and research so far has been promising. Algorithms inretrospective studies have performed equal or better to standard protocols such as SIRS or SOFA. Some promising research even presents the opportunity to approach sepsis diagnosis and treatment in an entirely new manner. At the present stage, however, the field is at too early a stage for use in a clinical environment. This review intends to review some prominent types of machine learning algorithms, as well as discuss current concerns regarding machine learning-based detection support systems (ML-DSS).